GENETIC ENHANCEMENT, SOCIAL JUSTICE, AND WELFARE‐ORIENTED PATTERNS OF DISTRIBUTION
Bibliographic record
Abstract
The debate over the host of moral issues that genetic enhancement technology (GET) raises has been significant. One argument that has been advanced to impugn its moral legitimacy is the 'unfair advantage argument' (UAA), which states: allowing access to GET to be determined by socio-economic status would lead to unjust outcomes, namely, create a genetic caste system, and with it the exacerbation and perpetuation of existing socio-economic inequalities. Fritz Allhoff has recently objected to the argument, the kernel of which is that it conflates the use of the technology with its distribution. GET, he argues, would generate unjust outcomes only if it is distributed according to principles of an unjust pattern of distribution; for if we can determine what constitutes a 'just' distributive scheme, then the technology can be allocated according to the principles of that scheme. In this paper I argue the following cluster of related claims: (1) both UAA and Allhoff's proposed distributive schemes ignore the importance of non-genetic factors in the development of an individual's characteristics and capacities; (2) if we accept the view that it is good to prevent unjust outcomes that arise because some have exclusive access to GET, then we have to accept wide-ranging distributive schemes; (3) by tracking genetic and non-genetic factors wide-ranging schemes do violate in some sense the widely shared value of neutrality in liberal democracies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.065 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".